Optimized individual sleep patterns

ABSTRACT

Embodiments of the invention are directed to a computer-implemented method for generating a sleep optimization plan. A non-limiting example of the computer-implemented method includes receiving, by a processor, genetic data for a user. The method also includes receiving, by the processor, Internet of Things (IoT) device data for the user. The method also includes generating, by the processor, a sleep duration measurement for the user based at last in part upon the IoT device data. The method also includes generating, by the processor, a sleep optimization plan for the user based at least in part upon the genetic data.

DOMESTIC AND/OR FOREIGN PRIORITY

This application is a continuation of U.S. application Ser. No.15/631,064, titled “Optimized Individual Sleep Patterns” filed Jun. 23,2017, the contents of which are incorporated by reference herein in itsentirety.

BACKGROUND

The present invention relates in general to sleep patterns, and morespecifically to optimized individual sleep patterns.

The duration and quality of sleep can impact a number of factorspertaining to health and well-being. A sufficient amount of sleep, forexample, is required for optimal brain function and attention. Forinstance, even relatively minor sleep deficits can lead tounderperformance at work or school. It has been shown, for example, thatmissing only one to two hours of sleep can as much as double the risk ofa car accident. Sleep is not only associated with brain function andattention, but can also be linked with proper immune system functioning,obesity, and mood.

Optimizing sleep patterns to provide optimal recommendations for healthand well-being can be challenging. Sleep quality and duration, forexample, can be highly individualized. It is frequently observed, forexample, that the sheer number of hours of sleep required for anindividual to consider him or herself rested varies from person toperson. Moreover, sleep duration and quality can be affected by externalfactors and biological factors. For example, research indicates thatexposure to light not only during sleeping hours, but also in the hourspreceding sleep, can adversely impact sleep quality.

Measuring the quality and duration of sleep can also pose a number ofchallenges. For example, movement-based sensors worn by individuals canin some cases distinguish between periods of wakefulness and sleep, butcan be subject to erroneous readings when individuals move during sleep.In addition, the degree and duration of movement during sleep can alsovary from person to person, further complicating motion-based sleepmeasurements.

SUMMARY

Embodiments of the invention are directed to a computer-implementedmethod for generating a sleep optimization plan. A non-limiting exampleof the computer-implemented method includes receiving, by a processor,genetic data for a user. The method also includes receiving, by theprocessor, Internet of Things (IoT) device data for the user. The methodalso includes generating, by the processor, a sleep duration measurementfor the user based at last in part upon the IoT device data. The methodalso includes generating, by the processor, a sleep optimization planfor the user based at least in part upon the genetic data. Suchembodiments can provide personalized sleep optimization for anindividual to improve overall health and well-being.

Embodiments of the invention are directed to a computer program productfor generating a sleep optimization plan. The computer program productincludes a computer readable storage medium readable by a processingcircuit and storing program instructions for execution by the processingcircuit for performing a method. A non-limiting example of the methodincludes receiving genetic data for a user. The method also includesreceiving IoT device data for the user. The method also includesgenerating a sleep duration measurement for the user based at last inpart upon the IoT device data. The method also includes generating asleep optimization plan for the user based at least in part upon thegenetic data. Such embodiments can provide personalized sleepoptimization for an individual to improve overall health and well-being.

Embodiments of the invention are directed to a processing system forgenerating a sleep optimization plan. The processor is in communicationwith one or more types of memory. In a non-limiting example of theprocessing system, the processor is configured to receive genetic datafor a user. The processor is also configured to receive IoT device datafor the user. The processor is also configured to generate a sleepduration measurement for the user based at last in part upon the IoTdevice data. The processor is also configured to generate a sleepoptimization plan for the user based at least in part upon the geneticdata. Such embodiments can provide personalized sleep optimization foran individual to improve overall health and well-being.

Embodiments of the invention are directed to a computer-implementedmethod for generating a sleep duration estimate. A non-limiting exampleof the computer-implemented method includes receiving by a processor,sleep-motion genetic data for a user. The method also includes sensing,by a motion sensor in an IoT network, user motion. The method alsoincludes generating, by the processor, a preliminary wakeful periodidentification based at least in part upon the sensed user motion. Themethod also includes generating, by the processor, a corrected wakefulperiod identification based at least in part upon the sleep-motiongenetic data. The method also includes generating, by the processor, auser sleep duration estimate based at least in part upon the correctedwakeful period identification. Such embodiments can provide accurate andreliable measurements of sleep duration and avoid erroneous readings dueto variable limb movements during sleep.

Embodiments of the invention are directed to a system for optimizingsleep patterns. A non-limiting example of the system includes aplurality of IoT sensors. The system can also include a geneticinformation database. The system can also include a sleep analyticsengine in communication with the plurality of IoT sensors and thegenetic information database. The system can also include an outputinterface. Such embodiments can provide convenient sleep optimizationwithout need for clinical settings or cumbersome equipment.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter of the present invention is particularly pointed outand distinctly claimed in the claims at the conclusion of thespecification. The foregoing and other features and advantages of theone or more embodiments described herein are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 3 depicts a computer system according to one or more embodiments ofthe present invention.

FIG. 4 depicts a diagram illustrating an exemplary system for optimizingsleep patterns according to one or more embodiments of the presentinvention.

FIG. 5 is a flow diagram illustrating a method for generating a sleepduration estimate according to one or more embodiments of the presentinvention.

FIG. 6 is a flow diagram illustrating a method for generating a sleepoptimization plan according to one or more embodiments of the presentinvention.

FIG. 7 is a flow diagram illustrating another method for generating asleep optimization plan according to one or more embodiments of thepresent invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

In the accompanying figures and following detailed description of thedescribed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers. With minorexceptions, the leftmost digit(s) of each reference number correspond tothe figure in which its element is first illustrated.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” can include any integer number greater than or equalto one, i.e. one, two, three, four, etc. The terms “a plurality” caninclude any integer number greater than or equal to two, i.e. two,three, four, five, etc. The term “connection” can include both anindirect “connection” and a direct “connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

It is understood in advance that although this description includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model can includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but can be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It can be managed by the organization or a third party andcan exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It can be managed by the organizations or a third partyand can exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure including a networkof interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50according to one or more embodiments of the present invention isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N can communicate. Nodes 10 cancommunicate with one another. They can be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) according to one or moreembodiments of the present invention is shown. It should be understoodin advance that the components, layers, and functions shown in FIG. 2are intended to be illustrative only and embodiments of the inventionare not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments of the invention, softwarecomponents include network application server software 67 and databasesoftware 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities can be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 can provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources can include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment can be utilized. Examples of workloads andfunctions which can be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and optimizing individual sleep patterns 96.

Referring now to FIG. 3, a schematic of a cloud computing node 100included in a distributed cloud environment or cloud service network isshown according to one or more embodiments of the present invention. Thecloud computing node 100 is only one example of a suitable cloudcomputing node and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the invention describedherein. Regardless, cloud computing node 100 is capable of beingimplemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 100 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that can besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 can be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules can includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 can be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules can be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 3, computer system/server 12 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 can include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media can be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 can further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 can include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,can be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, can include an implementation of a networkingenvironment. Program modules 42 generally carry out one or morefunctions and/or methodologies in accordance with some embodiments ofthe present invention.

Computer system/server 12 can also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.,one or more devices that enable a user to interact with computersystem/server 12, and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, the quality and duration of sleepon a daily basis and over time can be a critical influence on thehealth, productivity, and safety of individuals. Lack of sleep, forexample, is frequently associated with poor performance at work orschool. Moreover, persistent patterns of poor or insufficient sleep canadversely impact health and well-being.

Accurate and reliable sleep measurement and optimization has potentialto benefit the health and well-being of a large population ofindividuals. A sufficient amount of sleep, for example, is required foroptimal brain function, attention, and memory, and can also contributeto proper immune function, weight management and mood. Tracking andoptimizing sleep patterns is not only of interest to individuals, forinstance who wish to ensure their own individual optimal health andbrain function, but also to employers, teachers, and the population atlarge, where inattentiveness and cognitive impairments can detrimentallyimpact the workplace, classrooms, and even road safety.

The quantity of sleep required for optimal performance and functioningcan vary from person to person and can be impacted by genetic andenvironmental factors. For instance, it is widely understood that someindividuals require only 7 hours of sleep per night, while others canrequire 8 hours of sleep per night.

In addition, for any given individual, the duration and quality of sleepon a day to day basis can be influenced by a number of environmentalfactors including, for instance, light exposure, caffeine consumption,and physical exertion. For example, it is known that physical exertionduring the day can increase the amount of sleep required for optimalfunctioning. Increased physical exertion can also be associated with abetter quality of sleep. Moreover, exposure to light, including light ofcertain characteristics such as frequency or intensity, both duringsleep and before sleep can detrimentally impact the quality andquantity, or duration, of sleep for an individual. For example, exposureto high frequency and/or high intensity light two to three hours beforesleep can be associated with lower melatonin levels. Lower melatoninlevels can decrease the quality of sleep. In addition, periods ofwakefulness and sleep can fluctuate over the course of a night for somepeople as well as movement during times of sleep.

A number of genetic factors can affect sleep. Individuals with a variantin the limb-movement associated genes, such as BTB Domain Containing 9(BTBD9) gene for example, tend to move their limbs more during sleep inrelation to individuals without the variant (e.g., 12 movement periodsper night versus 7 movement periods). Individuals with a variant in theadenosine deaminase (ADA) gene (e.g., rs73598374 polymorphism) can havehigher adenosine levels and, as a result, can experience higher qualitysleep in relation to individuals without the ADA variant. In addition,individuals with a variant in the ADA gene can require relatively largerquantities of sleep in a given night than those without the variant. Thecytochrome P450 1A2 (CYP1A2) gene and aryl hydrocarbon receptor gene(AHR) gene can also affect sleep through metabolism of caffeine.Specifically, individuals with a variant in one of the CYP1A2 gene orthe AHR gene can metabolize caffeine at twice the rate of those with novariant in either gene. Individuals with variants in both of the CYP1Aand AHR genes can metabolize caffeine at four times the rate of thosewith no variant in either gene.

Genetic factors can play a role in both individualized sleeprequirements and also in obtaining accurate measurements. For example,caffeine consumption is widely known to impact sleep, but recent studieshave shown that some individuals can be genetically predisposed tometabolize caffeine more quickly than others. In addition, studies haveassociated variants in an ADA gene with more intense deep sleep and, atthe same time, less movement during periods of sleep.

An accurate measurement of the duration of sleep can contribute to areliable sleep optimization system. For instance, the duration and/orquantity of sleep of an individual on an ongoing basis can aid indetermining not only the presence or absence of a sleep deficit, but canprovide an assessment of the amount of sleep needed for an individual.

Although accurate measurements of the quantity and/or duration of sleepcan be important, such measurements can be difficult to obtain outsideof a clinical or controlled setting. Internet of Things (IoT) devicescan include a variety of information gathering or sensing components,such as motion sensors, accelerometers, heart rate monitors, and lightsensors, that can aid in the measurement of factors pertaining to sleep.The IoT is an object-interconnecting network that can include varioussensing devices. Wearable or nearby motion sensing devices, for example,can identify and associate periods of inactivity with periods of sleep.However, erroneous readings can result, for instance, where individualsexperience movement during sleep if the motion sensing device associatessuch sleep-movements with periods of wakefulness.

In addition, because of the individualized nature of various aspectspertaining to sleep quality and quantity, providing reliablerecommendations to optimize sleep patterns remains challenging.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings ofthe prior art by providing sleep monitoring that tracks motion over aperiod of time and corrects observed motion for genetic factorspertaining to sleep movement, such as the BTBD9 gene. Embodiments of theinvention can provide an assessment of sleep quality that considersgenetic factors, such as the presence or absence of a variant in the ADAgene. Embodiments of the invention can optimize sleep patterns byproviding individualized sleep recommendations based upon individualizedgenetic information, environmental information, and/or daily activityinformation.

The above-described aspects of the invention address the shortcomings ofthe prior art by providing more accurate or reliable individualizedmeasurements of sleep quantity and/or quality. Embodiments of theinvention can optimize individual sleep patterns by generatingpersonalized recommendations that include recommended sleep quantity,caffeine consumption guidelines, physical exertion and/or light exposureguidelines. Embodiments of the invention can provide a reliableidentification of a sleep deficit.

Referring now to FIG. 4, a diagram illustrating an exemplary system 400for optimizing sleep patterns according to one or more embodiments ofthe present invention is shown. The system 400 can include an internetof things (IoT) 402. The internet of things 402 can include aninter-connected network of devices including a plurality of sensors. TheIoT 402 can include, for example, one or more of a motion sensor 412,light sensor 414, user input interface 416, heart rate monitor 418, orpedometer 420. The system 400 can include a sleep analysis module 404 incommunication with the IoT 402. The sleep analysis module 404 isoptionally in communication with a genetic information database 406. Thesleep analysis module 404 can communicate with or can be included inwhole or part in the cloud 410. The sleep analysis module 404 interactswith a sleep analytics engine 424. The sleep analytics engine 424 cancommunicate with a sleep analytics database 422, which can store datapertaining to sleep analysis, including analysis data, sleepmeasurements, and sleep recommendations for the user and optionally forother users, such as users with similar demographic or genetic profiles.In some embodiments of the invention, the sleep analytics engine 424 andsleep analytics database 422 are in the cloud 410.

The motion sensor 412 can include any sensor capable of detecting userbody movement, such as an accelerometer, gyroscope, compass, pressuresensor, light-based sensors, or combinations thereof. The motion sensor412 can be included in one or more devices in the IoT 402, such as smartphones, wearable devices such as smart watches or wearable wrist bands,or stand-alone devices. The IoT can include wired or wirelesscommunication between a plurality of devices, including, for instance,Bluetooth, near-field communication (NFC), Wi-Fi, radio-frequency (RF),Ethernet, and the like.

The light sensor 414 can include sensors that detect light frequencyand/or light intensity. In embodiments of the invention, the IoT 402 caninclude one or more other sensors, including any other sensors that canbe useful in assessing sleep movement, physical exertion, and/orexternal or environmental conditions, such as thermometers, hygrometers,barometers, or microphones.

The user input interface 416 can include a keyboard, touch sensitivedisplay, microphone, or other means for providing input to the system400. The user input interface 416, in some embodiments of the invention,can receive caffeine intake data for a user, for example by manual inputfrom a user when caffeine is consumed.

In embodiments of the invention, the genetic information database 406includes genetic information for the user. The genetic information inthe genetic information database 406 can be in the form of genesequences or in the form of identification of a presence or absence ofspecific gene variants. The genetic information can include, forexample, information identifying variants in the BTBD9 gene, the ADAgene, the CYP1A2 gene, and/or the AHR gene for a user. In someembodiments of the invention, access to the genetic information database406 can be controlled, restricted, or secured to protect user privacy.

The sleep analytics engine 424 can perform analytics and/or machinelearning techniques. The sleep analytics engine 424 can correlatedescriptors from sensor and genetic information using known methods,such as multiple linear regression, partial least squares fit, SupportVector Machines, and random forest. The sleep analytics engine 424 cangenerate sleep measurements and/or sleep recommendations.

FIG. 5 depicts a flow chart of an exemplary method 500 for generating auser sleep duration estimate according to one or more embodiments of thepresent invention. The method 500 includes, as shown at block 502,receiving sleep-motion genetic data for a user. Sleep-motion geneticdata can include, for example, an identification of the presence orabsence of a gene polymorphism that is associated with limb movementduring sleep, such as a polymorphism in BTBD9, TOX3, BC034767, MEIS1,MAP2K/SKOR1, or PTPRD. The method 500 also includes, as shown at block504, sensing user motion with an IoT motion sensor. The method 500 alsoincludes, as shown at block 506, generating a preliminary wakeful periodidentification based at least in part upon the sensed user motion. Themethod 500 also includes, as shown at block 508 generating a correctedwakeful period identification based at least in part upon sleep-motiongenetic data. The method 500 also includes, as shown at block 510,generating a user sleep duration estimate based at least in part uponthe corrected wakeful period identification.

FIG. 6 depicts a flow chart of an exemplary method 600 for optimizingsleep patterns according to one or more embodiments of the presentinvention. The method 600 includes, as shown at block 602, receivingsleep-quality genetic data for a user. Sleep-quality genetic data caninclude, for example, an identification of the presence or absence of agene polymorphism that is associated with sleep quality for a user, suchas a polymorphism (rs73598374) in adenosine deaminase (ADA). The method600 also includes, as shown at block 604, receiving caffeine-metabolismgenetic data for the user. Caffeine-metabolism genetic data can include,for example, an identification of the presence or absence of a genepolymorphism that is associated with caffeine metabolism for a user,such as a polymorphism in CYP1A2 and/or AHR genes. The method 600 alsoincludes, as shown at block 606 receiving a sleep duration measurementfor the user. In some embodiments of the invention, the method includesreceiving a plurality of sleep duration measurements or a pattern ofsleep duration for the user. The method 600 also includes, as shown atblock 608 generating a sleep optimization plan output for the user basedat least in part upon the sleep-quality genetic data, the caffeinemetabolism genetic data, and/or the sleep duration measurement.

FIG. 7 depicts a flow chart of another exemplary method 700 foroptimizing sleep patterns according to one or more embodiments of thepresent invention. The method 700 includes, as shown at block 702,receiving physical exertion data from a first sensor in an IoT network.The physical exertion data can include, for example, heart rate data,accelerometer data, gyroscope data, altimeter data, temperature sensordata, bioimpedance data, and combinations thereof. The method 700 alsoincludes, as shown at block 704, receiving light exposure data from asecond sensor in an IoT network. The light exposure data can include,for example, light frequency and/or light intensity measurements. Thelight exposure data can be limited to a specific time range, for exampleto a time range associated with sleep of the user or with an averageuser, such as in the evening or at night or within 2 or 3 hours ofbedtime for the user or for an average user. The method 700 alsoincludes, as shown at block 706, generating a sleep analysis including alight recommendation and a sleep time recommendation based at least inpart upon the physical exertion data and the light exposure data. Thesleep analysis can include a determination of whether sleep patterns arelikely to be impacted by light exposure, physical exertion, or otherfactors. The method 700 also includes, as shown at block 708 outputtingthe sleep optimization plan including a light recommendation and a sleeptime recommendation to a user interface. A light recommendation caninclude, for example, a recommendation to avoid lighting of a givenfrequency or intensity within a specified time window. A sleep timerecommendation can include, for example, a suggested bed time, waketime, number of hours of sleep, or both.

In an exemplary scenario, a method can include receiving physicalexertion data during the day and classifying the physical exertion data,for instance using sleep analytics engine, as low, medium, or high). Theexemplary method can include receiving light exposure data within threehours before sleep onset and classifying the light exposure data as low,medium, or high. The exemplary method can include receiving dailycaffeine intake data and classifying the daily caffeine intake data aslow, medium, or high, for instance, relative to the user's historicalcaffeine consumption, relative to a threshold caffeine value, orrelative to a level derived from a plurality of individuals, such as anaverage user or an average user with a similar demographic or geneticprofile. Optionally, the method can include receiving, with userconsent, genetic information identifying variants in BTBD9, ADA, CYP1A2,and AHR. Exemplary data and exemplary sleep optimization plans for theexemplary data are summarized in the following table.

Output Physical Light Caffeine BTBD9 ADA CYP1A2 AHR measurement exertionexposure intake variant variant variant variant and plan High High HighYes No Yes Yes Adjust sleep duration upward due to BTBD9 No caffeinerecommendation based on genetic data Recommend reducing light exposurebefore sleep Low Medium Medium No Yes No No Adjust sleep durationdownward due to BTBD9 Increase total sleep duration requirement due toADA Recommend increased physical activity and lower caffeine Medium HighMedium Yes Yes Yes Yes Adjust sleep duration upward due to BTBD9 Nocaffeine recommendation based on genetic data Recommend reducing lightexposure before sleep Low Low High N/A N/A N/A N/A Recommend increasedphysical activity and lower caffeine High High Medium N/A N/A N/A N/ARecommend decreased light exposure and lower caffeine Low High Low N/AN/A N/A N/A Recommend increased physical activity and decreased lightexposure

Embodiments of the present invention can provide accurate sleep durationestimates by incorporating genetic data in a motion-based analysis.Embodiments of the invention can also provide individualized sleepoptimization plans that consider daily environmental factors, such aslight exposure, genetic information, such as caffeine metabolisminformation, and/or personalized activity information, such as dailyphysical exertion data. Embodiments of the invention can provideindividualized and reliable plans for optimizing the duration andquality of sleep for a user without clinical intervention or cumbersomeequipment.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments of the invention, electroniccircuitry including, for example, programmable logic circuitry,field-programmable gate arrays (FPGA), or programmable logic arrays(PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, element components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form described. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flow diagrams depicted herein are just one example. There can bemany variations to this diagram or the steps (or operations) describedtherein without departing from the spirit of embodiments of theinvention. For instance, the steps can be performed in a differing orderor steps can be added, deleted or modified. All of these variations areconsidered a part of the claimed invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments described. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method for generating asleep optimization plan, the method comprising: receiving, by aprocessor, genetic data for a user, the genetic data comprising apresence of a gene polymorphism in BTBD9, TOX3, BC034767, MEIS1,MAP2K/SKOR1, or PTPRD; receiving, by the processor, Internet of Things(IoT) device data for the user; generating, by the processor, a sleepduration measurement for the user based at last in part upon the IoTdevice data; correcting the sleep duration measurement based on thepresence of the gene polymorphism; and generating, by the processor, asleep optimization plan for the user based at least in part upon thegenetic data and the corrected sleep duration measurement.
 2. Thecomputer-implemented method of claim 1, wherein the genetic data issleep-quality genetic data.
 3. The computer-implemented method of claim1, wherein the sleep-quality genetic data comprises a determination of apresence or an absence of a polymorphism in adenosine deaminase.
 4. Thecomputer-implemented method of claim 1, wherein the IoT device datacomprises physical exertion data for the user.
 5. Thecomputer-implemented method of claim 1, wherein the physical exertiondata comprises data from the group consisting of heart rate data,accelerometer data, gyroscope data, altimeter data, temperature sensordata, bioimpedance data, and combinations thereof.
 6. Thecomputer-implemented method of claim 1, wherein the IoT device datacomprises light exposure data.
 7. The computer-implemented method ofclaim 1 further comprising receiving, by the processor, caffeine intakedata for the user.
 8. The computer-implemented method of claim 1 furthercomprising receiving, by the processor, caffeine-metabolism genetic datafor the user.
 9. The computer-implemented method of claim 1, wherein thecaffeine-metabolism genetic data comprises a determination of a presenceor an absence in a polymorphism in a cytochrome P450 1A2 gene, an arylhydrocarbon receptor gene, or both.
 10. The computer-implemented methodof claim 1 further comprising generating the sleep optimization plan ina cloud environment.
 11. A computer-implemented method for generating asleep duration estimate, the method comprising: receiving, by aprocessor, sleep-motion genetic data for a user, the sleep-motiongenetic data comprising a presence of a gene polymorphism in BTBD9,TOX3, BC034767, MEIS1, MAP2K/SKOR1, or PTPRD; sensing, by a motionsensor in an IoT network, user motion; generating, by the processor, apreliminary wakeful period identification based at least in part uponthe sensed user motion; generating, by the processor, a correctedwakeful period identification based at least in part upon the presenceof the gene polymorphism in the sleep-motion genetic data; andgenerating, by the processor, a user sleep duration estimate based atleast in part upon the corrected wakeful period identification.
 12. Thecomputer-implemented method of claim 11, wherein the sleep-motiongenetic data comprises a determination of a presence or absence of apolymorphism in a BTB Domain Containing 9 (BTBD9) gene.